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Structural Damage Prediction Under Seismic Sequence Using Neural Networks

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Structural Damage Prediction Under Seismic Sequence Using Neural Networks COMPDYN 2021 8th ECCOMAS Thematic Conference on Computational Methods in Structural Dynamics and Earthquake Engineering M. Papadrakakis, M. Fragiadakis (eds.) Streamed from Athens, Greece, 27—30 June 2021 STRUCTURAL DAMAGE PREDICTION UNDER SEISMIC SEQUENCE USING NEURAL NETWORKS Petros C. Lazaridis1, Ioannis E. Kavvadias1, Konstantinos Demertzis 1, Lazaros Iliadis1, Antonios Papaleonidas1, Lazaros K. Vasiliadis1, Anaxagoras Elenas1 1Department of Civil Engineering, Democritus University of Thrace, Campus of Kimmeria, 67100 Xanthi, Greece e-mail: fpetrlaza1@civil, ikavvadi@civil, kdemertz@fmenr, liliadis@civil, papaleon@civil, lvasilia@civil, [email protected] Keywords: Seismic Sequence, Neural Networks, Repeated Earthquakes, Structural Damage Prediction, Artificial Intelligence, Intensity Measures Abstract. Advanced machine learning algorithms, such as neural networks, have the potential to be successfully applied to many areas of system modelling. Several studies have been al- ready conducted on forecasting structural damage due to individual earthquakes, ignoring the influence of seismic sequences, using neural networks. In the present study, an ensemble neural network approach is applied to predict the final structural damage of an 8-storey reinforced concrete frame under real and artificial ground motion sequences. Successive earthquakes con- sisted of two seismic events are utilised. We considered 16 well-known ground motion intensity measures and the structural damage that occurred by the first earthquake as the features of the machine-learning problem, while the final structural damage was the target. After the first seismic events and after the seismic sequences, both actual values of damage indices are calcu- lated through nonlinear time history analysis. The machine-learning model is trained using the dataset generated from artificial sequences. Finally, the predictive capacity of the fitted neural network is accessed using the natural seismic sequences as a test set. P.C. Lazaridis, I.E. Kavvadias, K. Demertzis, L. Iliadis, A. Papaleonidas, L.K. Vasiliadis, A. Elenas 1 INTRODUCTION In recent years advanced Machine Learning (ML) algorithms, such as Artificial Neural Net- works (ANN), has been successfully applied to many areas of system modelling. Their success is based on the thorough processing of data that captures the behaviour of a system. By detect- ing patterns in the collected data, valuable information can be extracted and predictions can be made that automate the decision-making process. That fact arrives ML as an advanced tool in modern engineering modelling. The object of the present research is the application of neural networks to predict the struc- tural damage under seismic sequences. The recent disaster held on March 2021 in Tyrnavos- Elassona region, Thessaly of Greece due to a pair of compatible magnitude (Mw=6.3, Mw=6.1) [1, 2] shallow earthquakes with more than 1800 damaged or non-serviceable buildings, indicate the critical role of seismic sequences in the final damage state of buildings again. In the field of seismic sequences have been conducted many kinds of research to access the contribution of repeated ground motions on the post-seismic performance state of a building so far, which con- cluded that aftershocks increase the ductility demands. Amadio et al. [3] studied the influence of repeated shocks on the response of nonlinear Single Degree of Freedom (SDOF) systems using different hysteretic models. Hatzigeorgiou and Beskos [4] conducted an exhausting parametric study on SDOF systems and proposed an empirical relation to calculate inelastic displacement ratio under repeated earthquakes. Hatzigeorgiou and Liolios [5] examined the nonlinear be- haviour of Reinforced Concrete (RC) frames subjected to multiple shocks considering a set of eight frames that varied both at height regularity and dimensioning practice. Hatzivassiliou and Hatzigeorgiou [6] studied the accumulation of damage and ductility demands due to seismic se- quence on three dimensional RC structures. Hosseinpour and Abdelnaby [7] studied the impact of different aspects, as earthquake direction, aftershock polarity and the influence of the vertical component, on the nonlinear response of RC frames under successive earthquakes. Also, more recently, Kavvadias et al. [8] and Zhou et al. [9] investigated the correlation between aftershock related Intensity Measures (IMs) and final structural damage indices. Previous several studies have been conducted on forecasting of structural damage due to in- dividual earthquakes, ignoring the influence of seismic sequences. De Latour and Omenzetter [10] investigated the efficiency of ANNs on the prediction of seismic damage on numerous RC frames, Alvanitopoulos et al. [11] also examined regular RC structures and, they incorporate fuzzy layers in ANN configuration (architecture). Later, Morfidis and Kostinakis [12] used fea- ture selection methods in a dataset of 3 dimensional RC buildings to found the more damage correlated set of seismic IMs. More recently, the same authors [13] examined the effective- ness of ANNs on the prediction of damage of non-regular at height structures. Applications of Recurrent Neural Networks (RNNs) on Earthquake Engineering made by Gonzalez´ et al. [14] and Mangalathu and Burton [15], also Zhang et al. [16] developed a LSTM (Long-Short Term Memory) network to predict structural response. Convolutional Neural Networks (CNNs) applied by Li et al. [17] and Oh et al. [18] for the same purpose. In the present study, ANNs are applied, considering real and artificial sequences of double seismic shocks, to predict the amount of final structural damage on an already damaged 8-storey RC frame after a first shock. We use 16 well-known ground motion IMs and the established damage from the first earthquake as the features of the ML problem, while the final structural damage (after sequence) employed as the target. Both damage indices after the first seismic event and after the sequence are calculated through Nonlinear Time History Analysis (NTHA). P.C. Lazaridis, I.E. Kavvadias, K. Demertzis, L. Iliadis, A. Papaleonidas, L.K. Vasiliadis, A. Elenas 2 SELECTION OF PRIMITIVE DATA In this section, the primitive data of the problem is described. Specifically, the selection of the ground motion records and the examined RC frame is presented. 2.1 Ground motion records Both artificial and natural seismic sequences are considered to take into account, the phe- nomenon of multiple shocks. After the random combination from a suite of 318 acceleration records from several earthquakes, the set of artificial sequences determined. More specifically, every record of the above 318 records suite is combined randomly three times with another record from the same dataset. As a result, 954 pairs of first and second shock are constructed, which are utilised as a training set for the specific ML problem. As a test set, we employed 119 natural pairs of sequential shocks, which occurred from 1972 to 2020. The training and test sets are completely independent with no common record. Both sequential and individual records are selected and downloaded from ESM [19] and PEER NGA West [20] databases. The criteria according to the selection of natural sequences conducted are the time gap between the occurrences of successive shocks which are considered smaller than three months and that the sequential events are recorded by the same station. As a result of this process collected 119 pairs of successive ground motion acceleration records from 41 real seismic sequences, recorded by 65 stations. The occurrences time and Peak Ground Acceleration (PGA) data of the natural sequences listed in Table 1. Every pair concatenated in the same array to compose the sequential ground acceleration time history. In addition, an intermediate zero ceasing time gap of 20 seconds added to eliminate the overlap between the building oscillations due to suc- cessive earthquakes. This process is implemented in Python [21], and the new files containing the seismic sequence acceleration signals are exported. 2.2 Reinforced concrete structure Existing buildings designed and constructed without earthquake provisions comprise the ma- jority of structures both in Greece and worldwide, which causes particular concern about their response to a potential earthquake. As an example, in this study we examined an 8-storey pla- nar regular RC frame (Figure 1) designed only for gravity loads by Hatzigeorgiou and Liolios [5], with mean compressive strength of concrete equal to 28 MPa and steel grade S500s. The finite element simulation of the frame held in IDARC 2D [22], using spread plasticity con- cept and three-parameter Park hysteretic model [23]. Every floor considered to have only one horizontal degree of freedom to into account the huge plane stiffness of RC slab as a rigid diaphragm. Sparsely placed stirrups with poor anchor details are assumed in order to be in ac- cordance with obsolete design codes. Thus, a nonlinear deformation-stress model for concrete without confinement is adopted. As a result, the corresponding concrete curve defined by the initial modulus of elasticity (E0 = 31:42GPa), the strain at the maximum stress (ec0 = 2h), the ultimate strain in compression (ecu = 3:5h), the stress at tension cracking st = 0:0022GPa, and the slope of the post-peak falling branch (E f b = −6:2GPa). Also, the bilinear curve with hardening employed for steel with yield and ultimate strengths equal to 550 MPa and 660 MPa respectively and the corresponding strains equal
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